Leisure sedentary behaviour increases the risk of venous thromboembolism: a Mendelian randomisation study.

BACKGROUND: Venous thromboembolism (VTE) is a substantial contributor to the global burden of disease. Observational studies have suggested that leisure sedentary behaviours (LSB) are related to the risk of VTE; however, the causal role of LSB in VTE remains unclear. METHODS: Using data obtained from genome-wide association studies in the UK Biobank (N = 422,218), we identified 84, 21, and 4 single nucleotide polymorphisms (SNPs) related to sedentary television (TV) watching, computer use, and driving, respectively. These SNPs were employed as instrumental variables. Summary statistics for SNP-VTE associations was obtained from the FinnGen study (5,403 cases and 130,235 controls). Two-sample Mendelian randomisation (MR) analyses were performed using inverse-variance weighted (IVW), MR-Egger,weighted median, and weighted mode approaches. Sensitivity analyses were conducted to ensure robustness of the results. RESULTS: The main IVW approach demonstrated a positive association between the genetically predicted sedentary TV watching and the risk of VTE [odds ratio (OR):1.35, 95% confidence interval (CI):1.02-1.80, P = 0.039]. However, no significant association was observed for genetically predicted sedentary computer use or driving and VTE risk. The results from our series of sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-Pleiotropy RESidual Sum and Outlier method, further supported these findings. CONCLUSION: This study provides evidence of an association between genetically predicted sedentary TV watching and the risk of VTE. Further studies are required to elucidate the underlying causal mechanisms.

Prognostic nomogram for 30-day mortality of deep vein thrombosis patients in intensive care unit.

BACKGROUND: We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU). METHODS: Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation. RESULTS: We obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI] > 0; Integrated discrimination improvement [IDI] > 0; P < 0.05). The Brier scores of two new models in training set were 0.091 and 0.108. After internal validation, corrected area under the curves for two models were 0.850 and 0.830, while corrected Brier scores were 0.108 and 0.114. The more concise model was chosen to make the nomogram. CONCLUSIONS: The nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.

Association between anion gap and mortality of aortic aneurysm in intensive care unit after open surgery.

BACKGROUND: There has not been a well-accepted prognostic model to predict the mortality of aortic aneurysm patients in intensive care unit after open surgery repair. Otherwise, our previous study found that anion gap was a prognosis factor for aortic aneurysm patients. Therefore, we wanted to investigate the relationship between anion gap and mortality of aortic aneurysm patients in intensive care unit after open surgery repair. METHODS: From Medical Information Mart for Intensive Care III, data of aortic aneurysm patients in intensive care unit after open surgery were enrolled. The primary clinical outcome was defined as death in intensive care unit. Univariate analysis was conducted to compare the baseline data in different groups stratified by clinical outcome or by anion gap level. Restricted cubic spline was drawn to find out the association between anion gap level and mortality. Subgroup analysis was then conducted to show the association in different level and was presented as frost plot. Multivariate regression models were built based on anion gap and were adjusted by admission information, severity score, complication, operation and laboratory indicators. Receiver operating characteristic curves were drawn to compare the prognosis ability of anion gap and simplified acute physiology score II. Decision curve analysis was finally conducted to indicate the net benefit of the models. RESULTS: A total of 405 aortic aneurysm patients were enrolled in this study and the in-intensive-care-unit (in-ICU) mortality was 6.9%. Univariate analysis showed that elevated anion gap was associated with high mortality (P value < 0.001), and restricted cubic spline analysis showed the positive correlation between anion gap and mortality. Receiver operating characteristic curve showed that the mortality predictive ability of anion gap approached that of simplified acute physiology score II and even performed better in predicting in-hospital mortality (P value < 0.05). Moreover, models based on anion gap showed that 1 mEq/L increase of anion gap improved up to 42.3% (95% confidence interval 28.5-59.8%) risk of death. CONCLUSIONS: The level of serum anion gap was an important prognosis factor for aortic aneurysm mortality in intensive care unit after open surgery.

Drug repurposing opportunities for chronic kidney disease.

The development of targeted drugs for the early prevention and management of chronic kidney disease (CKD) is of great importance. However, the success rates and cost-effectiveness of traditional drug development approaches are extremely low. Utilizing large sample genome-wide association study data for drug repurposing has shown promise in many diseases but has not yet been explored in CKD. Herein, we investigated actionable druggable targets to improve renal function using large-scale Mendelian randomization and colocalization analyses. We combined two population-scale independent genetic datasets and validated findings with cell-type-dependent eQTL data of kidney tubular and glomerular samples. We ultimately prioritized two drug targets, opioid receptor-like 1 and F12, with potential genetic support for restoring renal function and subsequent treatment of CKD. Our findings explore the potential pathological mechanisms of CKD, bridge the gap between the molecular mechanisms of pathogenesis and clinical intervention, and provide new strategies in future clinical trials of CKD.

Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images.

Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.

Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study.

Background: Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months. Materials and methods: The clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold. Results: In total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76-0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points. Conclusion: Machine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.

Development of SAB model for predicting mortality in intensive care unit after aortic aneurysm surgery.

BACKGROUND: Aortic aneurysm (AA) patients after vascular surgery are at high risk of death, some of them need intensive care. Our aim was to develop a simplified model with baseline data within 24 hours of intensive care unit (ICU) admission to early predict mortality. METHODS: Univariate analysis and least absolute shrinkage and selection operator were used to select important variables, which were then taken into logistic regression to fit the model. Discrimination and validation were used to evaluate the performance of the model. Bootstrap method was conducted to perform internal validation. Finally, decision clinical analysis curve was used to test the clinical usefulness of the model. RESULTS: We obtained baseline data of 482 AA patients from Medical Information Mart for Intensive Care III database, 33 (6.8%) of whom died in ICU. Our final model contained three variables and was called SAB model based on initials of three items [Sepsis, Anion gap, Bicarbonate (SAB)]. Area under the curve of SAB was 0.904 (95% CI: 0.841-0.967) while brier score was 0.043 (95% CI: 0.028-0.057). After internal validation, corrected area under the curve was 0.898 and brier score was 0.045, which showed good prediction ability of SAB model. The model can be assessed on https://vascularmodel.shinyapps.io/AorticAneurysm/. CONCLUSIONS: SAB model derived in this study can be easily used to predict in-ICU mortality of AA patients after surgery precisely.